本文選題：結構健康監(jiān)測 + 稀疏自適應的時頻分析方法��； 參考：《哈爾濱工業(yè)大學》2015年碩士論文

【摘要】：疲勞累積損傷是實際橋梁結構發(fā)生損傷破壞的主要原因之一,對于纜索類橋梁,拉索是其關鍵受力構件,拉索的累積疲勞損傷嚴重威脅橋梁結構的安全。傳統(tǒng)的基于振動法的索力測試,只能識別橋梁拉索在一段時間內的索力平均值。然而,橋梁拉索由于車輛荷載和環(huán)境因素的作用,其索力是時變的。時變索力是引起疲勞損傷的主要原因,同時也是進行拉索極限狀態(tài)安全評定和疲勞累積損傷評價的基礎。為此,本文研究基于無線傳感器和稀疏時頻分析的橋梁拉索時變索力識別方法。本文主要研究內容包括:基于稀疏自適應的時頻分析方法,提出一種新的識別時變索力的方法。稀疏自適應的時頻分析方法是當前信號處理領域新發(fā)展的方法,其原理是通過在最大的時頻分析字典里優(yōu)化尋找信號的最稀疏分解得到該信號的瞬時頻率。首先,根據稀疏自適應的時頻分析方法,從測得的索的加速度識別出時變的模態(tài)頻率。然后,將橋梁拉索簡化為理想的張緊弦,依據經典弦振動理論建立的索力與頻率之間的關系進行索力識別。利用索的振動存在的倍頻關系,減少方法的優(yōu)化變量,提高計算效率、降低噪聲對索的不同階瞬時頻率的影響、提高時變模態(tài)頻率識別的精度。最后將識別得到的索的時變模態(tài)頻率,代入索力公式得到拉索的時變的索力。采用拉索模型的有線傳感器試驗數據,考慮不同的索力變化工況,研究索力變化水平和速度對于本文提出方法識別精度的影響。然后考慮采用無線傳感器可能產生的數據丟失問題,利用有線傳感器測得的加速度數據模擬可能發(fā)生的數據丟失的不同情況,基于丟失后的數據進行索力識別,研究數據丟失的模式和數據丟失率對于該改進的時頻分析方法索力識別結果的影響。研究采用Imote2無線傳感器進行拉索振動測試和索力識別,對拉索模型進行試驗并考慮不同的索力變化工況。由于無線傳感器易受外界環(huán)境干擾而產生數據丟失,在試驗條件下人為造成數據丟失,對沒有丟失和存在數據丟失的實驗數據分別進行稀疏自適應的時頻分析,根據時頻關系識別索力,并對比兩種情況下識別到的頻率以及索力,測試稀疏自適應的時頻分析方法在存在數據丟失的情況下對索力的識別的精度和魯棒性。并對廈門海滄大橋的吊索進行現(xiàn)場測試,利用Imote2無線傳感器,測試實橋吊索的振動加速度,采用論文提出的方法識別時變的模態(tài)頻率并計算時變索力,進一步驗證方法對實際橋梁拉索時變索力的識別效果。
[Abstract]:Fatigue cumulative damage is one of the main causes of damage and damage to the actual bridge structure. For cable bridges, the cable is the key component of the bridge. The cumulative fatigue damage of the cable is a serious threat to the safety of the bridge structure. The traditional cable force test based on the vibration method can only identify the cable force average of the cable in a period of time. The cable force of bridge is time-varying due to vehicle load and environmental factors. The time variant cable force is the main cause of fatigue damage, and it is also the basis of safety assessment and fatigue cumulative damage evaluation of cable. Therefore, this paper studies the time variation of bridge cables based on wireless sensor and sparse time-frequency analysis. The main contents of this paper are as follows: Based on the sparse adaptive time-frequency analysis method, a new method of identifying time variant cable force is proposed. The sparse adaptive time-frequency analysis method is a new development method in the field of signal processing, and its principle is to optimize the thinnest of finding the signal in the largest time-frequency analysis dictionary. The instantaneous frequency of the signal is obtained by sparse decomposition. First, according to the sparse adaptive time-frequency analysis method, the time-varying modal frequency is identified from the acceleration measured by the cable. Then, the bridge cable is simplified to an ideal tension string, and the cable force identification is carried out according to the relationship between the cable force and the frequency rate based on the classical string vibration theory. In order to improve the calculation efficiency, reduce the influence of the noise on the different instantaneous frequency of the cable and improve the accuracy of the time-varying modal frequency identification, the time-varying modal frequency of the identified cable is obtained, and the cable force of cable is obtained by using the cable force formula. The cable sensor using the cable model is used. The test data, considering the different cable force change conditions, study the influence of the cable force change level and speed on the method recognition accuracy. Then consider the data loss problem that the wireless sensor may produce, using the acceleration data obtained by the wired sensor to simulate the different data loss, based on the loss. The later data are identified by cable force, and the effects of data loss patterns and data loss rates on the results of the improved time-frequency analysis method are studied. Imote2 wireless sensor is used to test the cable vibration and cable force identification, and the cable model is tested and different cable force changes are taken into consideration. Data loss is caused easily by external environment interference. In experimental conditions, the data is lost, and the time-frequency analysis of the experimental data which is not lost and the data loss is sparse adaptive. According to the time frequency relation, the cable force is identified and the frequency and cable force identified in the two cases are compared, and the time frequency of the sparse adaptive time is tested. The analysis method is accurate and robust to the identification of cable force in the case of loss of data. In the field test of the sling of Xiamen Haicang Bridge, the vibration acceleration of the real bridge sling is tested by Imote2 wireless sensor, and the time-varying mode frequency and time variable cable force are identified by the method proposed in this paper, and the method is further verified. The identification effect of the variable cable force when the actual bridge is pulled.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U446

【參考文獻】

相關期刊論文 前1條

1 馬堅偉;徐杰;鮑躍全;于四偉;;壓縮感知及其應用:從稀疏約束到低秩約束優(yōu)化[J];信號處理;2012年05期

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本文編號：1798015